Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions
Abstract
:1. Introduction
- A comprehensive and extensively analyzed literature survey of the latest and fundamentally critical studies that address in detail fault tolerance approaches in WSNs.
- A new taxonomy that provides a comprehensive classification for fault tolerance techniques for research in this area was conducted within a significant time frame that has not been previously addressed in an extensive manner, which is 2016–May 2022.
- To identify and discuss the open issues deduced from the proposed taxonomy of comparison and the enhanced fault tolerance management architecture needed in WSN.
2. State of the Art Surveys and Motivation of Fault Tolerance Classification Methods
- The coverage of the articles is on certain specific areas of algorithms and classifications.
- The time scale of the related works under examination is within a specific time period that creates its respective constraints of future applicability.
- The absence or the duplication of state-of-the-art open issues related to fault tolerance in WSN.
- Many previous studies were related to a specific type of the WSN concept, such as Mobile Ad Hoc Network (MANET), Flying Ad Hoc Network (FANET), and Underwater Sensor Network (USN).
- Many studies were on a specific type of fault tolerance, such as fault tolerance via clustering approaches, fuzzy approaches, or statistical approaches.
- ⬝
- classifying fault in a sensor network based on new metrics, which are fault pattern and stability, network components, and fault-affected area.
- ⬝
- categorizing faults into five classes based on: behavior, time, components, the affected area, and layers.
- ⬝
- categorization of fault-tolerant components into three vital stages of the fault tolerance architecture which are: error detection, error diagnosis, and error recovery.
- ⬝
- proposing a new taxonomy for fault tolerance structure that encompasses general classes and subclasses based on their performance.
- ⬝
- defining the existing fault tolerance approaches and analyzing the most important steps in error detection, error diagnosis, and error recovery.
- What are the most critical faults impacting WSNs that need to be addressed?
- When it comes to WSNs, what are the basic fault management procedures?
- What are the main operations for each stage in WSN?
- What methods may provide a thorough classification for fault tolerance structure?
- What are the most significant difficulties associated with fault management?
- Are there any fault tolerance systems that need to be estimated or evaluated?
- Will fault management methods evolve, embracing new paradigms such as artificial intelligence (AI) and other features in the future?
3. Faults Classifications in WSN
4. The Main Aspects of Faults Management Structure in WSNs
4.1. Error Detection
4.2. Error Diagnosis
4.3. Error Recovery
5. Proposed Performance Parameters within Fault Tolerance Technique in WSNs
- ⬝
- Detection Accuracy (DA): The ratio between the successfully recognized faulty sensor nodes divided by the total number of actual defective nodes represents the detection accuracy [19]. Improving error detection accuracy is possible by growing the number of nodes that involved in the fault detection process inside a specific region [59]. Therefore, collaboration among all neighbors in the same event region for example will enhance error detection in general. Increasing fault detection time also increases accuracy even though it will cause greater delay and more energy cost.
- ⬝
- Energy Consumption: Energy consumption is considered one of the main issues in WSNs due to the limited power resource and the complexity or impossibility of replacing the power supplies for all nodes within the WSN [60]. Enhance the energy consumption and network failure control go hand in hand. Therefore, a fault tolerance system is needed to identify and recover problems with low energy usage [58,59]. Reducing the sending operations to the BS will play a vital role to improve energy consumption [61]. Less messaging reduces energy usage in fault control while Increasing fault detection accuracy increases energy usage.
- ⬝
- Delay: Is well-defined as the amount of time that elapses between the occurrence of a fault and the discovery of the error. A longer delay increases the likelihood of a failure spreading inside the network and affects entire network reliability as a consequence of the delay [62].
- ⬝
- Scalability: Many important aspects in WSNs such as fault tolerance and routing, should have the ability to be scalable. Scalability means the network’s capacity to accept more sensor nodes or cluster heads. The fault tolerance approach must be able to manage the high scale and small networks [24].
- ⬝
- Communication Cost: Total number of messages transmitted per node is the communication cost. Because of the significant effect of this activity on the network performance, several fault tolerance approaches have attempted to minimize communication costs to a minimum [59]. However, increased congestion, increased delay, and increased energy usage are all consequences of high communication costs.
- ⬝
- Network Lifetime: A network’s lifespan is defined as the period between network initiation and the moment when the first node dies in the network [63,64]. The fault-tolerance approaches have to take into account the network lifetime and try within its functionality to avoid minimizing the network lifespan.
- ⬝
- False Alarm Rate (FAR): The ratio between the number of faulty nodes that reported error reports to the total number of faulty nodes [59]. In many situations, there are special cases in which some nodes produce an error report towards the BS, especially with monitoring applications. Fault tolerance approaches have a harsh fight with the wrong fault alarms that consume energy, congest the network, and disturb the control center with incorrect readings [65,66]. Such a fake alarm will affect the network’s integrity and reliability.
6. Proposed Classification of Fault Tolerance Management Approaches in WSN
6.1. Centralized Fault Tolerance Approaches
6.2. Decentralized Fault Tolerance Approaches
6.3. Hybrid Fault Tolerance Approaches
7. Comparative Analysis, Discussion, and Open Issues
8. Open Research Issues
8.1. Energy Efficiency
8.2. Communication Overhead
8.3. Security
8.4. Scalability and Density Deployment
8.5. Latency
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Techniques | Contributions | Parameters Enhancement within Technique | |||||||
---|---|---|---|---|---|---|---|---|---|
Minimize Energy Use | Minimize Congestion | Minimize False Alarm Rate | Minimize Delay | Fault Detection Accuracy | Improved Costs | High Scalability | Maximize Network Lifespan | ||
[2] | Proposed a novel fault tolerance routing algorithm using a hybrid meta-heuristic algorithm which integrated the Firefly Optimization (FA) with Gray Wolf Optimization (GWO). | ✓ | ✓ | ✓ | ✓ | ||||
[7] | Proposed a novel method for detecting the random packet loss based on the Bernoulli distribution through the network from the sensors to the filters. The proposed method utilizes the IT2 T–S fuzzy model and a new distributed fault detection filter corresponding to the sensor nodes. | ✓ | ✓ | ✓ | |||||
[13] | Proposed a new approach based on artificial intelligence to handle the faults during data transmission to the BS. | ✓ | ✓ | ✓ | |||||
[14] | Proposed a novel Distributed Fault Detection (DFD) that recognizes the neighboring hot nodes and imposed their impact for fault detection. | ✓ | ✓ | ✓ | |||||
[37] | Proposed multiple solutions such as a Maximum Coverage Location Problem (MCLP) algorithm to find optimal locations for CH placement, a Multi-Objective Deep Reinforcement Learning (MODRL) for fault detection and fault-free optimal data routing path selection, and presented a mobile sink-based data gathering scheme for better reliability. | ✓ | ✓ | ✓ | ✓ | ||||
[45] | Proposed construction of a regular hexagonal-based clustering scheme (RHCS) of sensor networks and analyzed the reliability of RHCS based on the Markov model. Moreover, this work proposed a scale-free topology evolution mechanism. | ✓ | ✓ | ✓ | ✓ | ||||
[46] | Proposed a management framework that is qualified to provide network fault tolerance that detects and recovers mechanisms for various faults including network nodes and communications between them. The whole work was built on the idea of Check Point Node (CHN) and storing all data temporally. | ✓ | ✓ | ✓ | ✓ | ||||
[57] | Proposed a novel machine-learning-based architecture for detecting anomalies readings from sensors, identifying the faulty ones, and adapting them with suitable estimated data. | ✓ | ✓ | ✓ | |||||
[59] | Proposed the True Event-Driven and Fault Tolerance Routing (TED-FTR) approach for real-time applications in WSNs. | ✓ | ✓ | ✓ | ✓ | ✓ | |||
[82] | Proposed the Triple Modular Redundancy (TMR) to monitor radiation levels near and within a nuclear reactor. | ✓ | ✓ | ✓ | ✓ | ||||
[83] | Proposed the Extra Trees Based (ETB) to detect and diagnose different types of faults in an ideal time for WSNs. | ✓ | ✓ | ✓ | ✓ | ||||
[84] | Proposed the Energy Efficient cluster-based Fault-Tolerant Routing Protocol (EE-FT) that avoids node faults before they occur. | ✓ | ✓ | ✓ | ✓ | ✓ | |||
[85] | Proposed fault filtering approach to detect and filter out faulty nodes, making the localization process more fault tolerant. | ✓ | ✓ | ||||||
[86] | Proposed a K-Set Converging Algorithm (KSCA) to build fault tolerance that can deal with Delay Constrained Relay Node Placement. | ✓ | ✓ | ||||||
[87] | Proposed Trend Correlation-based Fault detection (TCFD) strategy to detect faulty nodes in WSNs. | ✓ | ✓ | ✓ | |||||
[88] | Proposed a push-flow algorithm for fault tolerance and employing the self-correcting properties of repeated improvement. | ✓ | ✓ | ||||||
[89] | Presented a comparison among three fault-tolerant routing protocols Multilevel, HDMRP, and EAQHSeN. | ✓ | ✓ | ✓ | |||||
[90] | Proposed an error guess, detection, and recovery algorithm using the Markov Chain Monte Carlo procedure for Underwater Wireless Sensor Networks (UW-WSN). | ✓ | ✓ | ||||||
[91] | Proposed Reliable Neuro-Fuzzy Optimization Model (RANDOM) for intra-cluster and inter-cluster fault detection. | ✓ | ✓ | ✓ | ✓ | ✓ | |||
[92] | Proposed a distributed fault-tolerant algorithm that deals with a finite number of transient errors based on Connected Dominating Set (CDS). | ✓ | ✓ | ✓ | |||||
[93] | Proposed fault-tolerant routing algorithm using Fractional Gaussian Firefly Algorithm (FGFA) and Darwinian Chicken Swarm Optimization (DCSO). | ✓ | ✓ | ✓ | ✓ | ||||
[94] | Proposed Directional NN algorithm directed to the next nearest node (NNNN) reduces data acquisition time while maintaining fault tolerance for links failures. | ✓ | |||||||
[95] | Proposed a path graph flow and Marchenko Pastur distribution for fault detection in cluster heads and normal nodes. | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
[96] | Proposed node faulty detection method to gain reliable communication in a wireless environment with a lot of obstacles. | ✓ | ✓ | ✓ | |||||
[97] | Proposed a fault tolerance technique to detect and diagnose faults, the backup nodes used to recover from faults. | ✓ | ✓ | ✓ | ✓ | ||||
[98] | Proposed a novel approach of decentralized detection over a Small World WSNs to utilize traffic flow between node pairs and result in a robust and low-complexity development. | ✓ | ✓ | ||||||
[99] | Presented a technique that is capable of diagnosing composite faults on sensor nodes and connections, including hard permanent, soft permanent, intermittent, and transient faults. | ✓ | ✓ | ✓ | |||||
[100] | Proposed an optimized Sup-port Vector Machine (SVM) for fault diagnosis in WSN based on the Gray Wolf Optimization (GWO) classifier that used to detect faults in sensor nodes | ✓ | ✓ | ||||||
[101] | Proposed energy-efficient fault-tolerance approach to enhance the reliability in the WBAN based on the cooperative communication and net-work coding strategy. | ✓ | ✓ | ||||||
[102] | Proposed a fault-tolerant approach named clustering-based DV Hop using K means clustering and majority voting methods. | ✓ | ✓ | ||||||
[103] | Proposed a new technique named Low Energy Fault Detection (LED) to utilize the sequence of data acquired by the sensor to detect certain types of faults. | ✓ | ✓ | ✓ | ✓ | ||||
[104] | Proposed a Fault detection method based on the Gaussian transformation algorithm to detect faulty nodes. | ✓ | ✓ | ||||||
[105] | Proposed and evaluated the trouble of detecting different kinds of fault data and the guidance of each type on event detection results. | ✓ | ✓ | ||||||
[106] | Proposed the two-stage error detection algorithms based on spatial-temporal cooperation performed by the BS in WSNs. | ✓ | ✓ | ||||||
[107] | Proposed a logical Cluster Head system in which the CH, like other nodes in the network, is prone to mistakes. The LEACH procedure has been updated to include intelligent dynamic CH selection based on residual energy and sensor inputs after each round. | ✓ | ✓ | ✓ | ✓ | ✓ | |||
[108] | Proposed a comparative study for noise, short-term, and fixed faults caused by low battery and calibration. The study was based on the performance of three popular algorithms which are: Support Vector Machine (SVM), Naive Bayes, and Gradient Lifting Decision Tree (GBDT). | ✓ | ✓ | ✓ | ✓ | ||||
[109] | Presented the hardware error diagnosis methods that detect the heterogeneous hardware errors such as unit, transmitter, and microcontroller. | ✓ | ✓ | ||||||
[110] | Proposed an error detection approach for Industrial Wireless Sensor Networks (IWSNs) based on software-defined networks (SDNs). | ✓ | ✓ | ✓ | |||||
[111] | Presented a heterogeneity fault diagnosis protocol via three steps to detect many kinds of errors such as hard, soft, and intermittent. | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
[112] | Presented a novel approach based on distributed detection and fuzzy logic to detect errors, isolate faulty nodes, and reuse some faulty nodes as relay nodes. | ✓ | ✓ | ||||||
[113] | Proposed fault detection method based on clustering to achieve high detection process run by CHs without bothering the BS. | ✓ | ✓ | ✓ | |||||
[114] | Proposed a high error detection approach based on double machine learning techniques, which are the neural networks and the Support Vector Machine (SVM). | ✓ | ✓ | ||||||
[115] | Proposed a novel distributed mobile sink-based fault diagnosis scheme for WSNs by using single hop communication. | ✓ | ✓ | ✓ | ✓ | ||||
[116] | Proposed a fuzzy multilayer with particle swarm optimization for fault detection in WSNs such as hard, soft, intermittent, and intermittent errors. | ✓ | ✓ | ||||||
[117] | Proposed a clustering-based method for fault tolerance using the genetic algorithm. | ✓ | ✓ | ||||||
[118] | Propose a new failure detection methodology for clustered WSNs named Efficient and Accurate Failure Detector (EAFD), which uses two degrees of suspicion to decide if a node has failed. | ✓ | ✓ | ||||||
[119] | Proposed a cluster-based fault detection and recovery method. False data detection is performed by estimating the accuracy value of each sensor node and then detecting and eliminating outliers. | ✓ | |||||||
[120] | Presented a method for preventing node failures by using the Ad hoc On-Demand Distance Vector (AODV) routing protocol and chick point recovery. | ✓ | ✓ | ||||||
[121] | Proposed a technique based on the Principal Component Analysis (PCA) to deal with information errors and redundant issues. | ✓ | ✓ | ||||||
[122] | Proposed comparative analysis for fault detection problem. The study evaluates six methods: Support Vector Machine (SVM), Convolutional Neural Network (CNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Probabilistic Neural Network (PNN). | ✓ | ✓ | ✓ | |||||
[123] | Proposed a practical cascading standard for WSNs, in which the load function is defined on each node according to a new directional traffic metric. The failed node can recover through a reboot after a specific time delay rather than being forever removed from the network. | ✓ | ✓ | ✓ | |||||
[124] | Presented a barrier coverage algorithm, namely Maximizing Cooperative Detection Probability (MCDP), which applies the Probability Sensing Model (PSM) and aims to perpetuate the life of solar-powered WSNs while maximizing the surveillance quality of the constructed barrier. The proposed method is based on calculating the detection probability of each sensor to each grid. | ✓ | ✓ | ||||||
[125] | Proposed a novel optimized fault-tolerant task allocation algorithm for IoT-WSNs called Discrete Particle Swarm Optimization (DPSO). The proposed algorithm employs a frame replication and elimination approach to transmit flow replicas over redundant routes and schedules the flow in time slots to avoid data corruption or the effect on the throughput. | ✓ | ✓ | ✓ | ✓ | ||||
[126] | Proposed a robust localization based on the Received Signal Strength Difference (RSSD) with unknown transmit power and Gaussian mixture noise in the presence of faulty nodes. A Robust Fault-Tolerant Localization (RFLT) technique is proposed also using a Generalized Trust-Region Subproblem (GTRS) framework. | ✓ | ✓ | ✓ | |||||
[127] | Presented a replicated gateway structure augmented with energy-efficient real-time Byzantine-resilient data communication protocols. The proposed method enhanced the geographic routing protocol capability of delivering messages in an energy-efficient, even in the presence of voids caused by faulty and malicious sensor nodes. | ✓ | ✓ | ✓ | |||||
[128] | Proposed a new classification approach for fault detection in WSNs. The proposed technique is based Support Vector Machines (SVMs) classification method SVM technique can detect many types of faults. | ✓ | ✓ | ||||||
[129] | Proposed a method for FT in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. The proposed approach used an Adapted Nondominated Sorting-based Genetic Algorithm (A-NSGA) to solve the optimization problem within network links. | ✓ | ✓ | ✓ | |||||
[130] | Proposed a bio-inspired Particle Multi-Swarm Optimization (PMSO) routing algorithm to create, recover, and elect k-disjoint paths that tolerate the failure while satisfying the quality-of-service parameters. The proposed work utilizes the use of Cumulative Distribution Function (CDF) for the sensors with an exponentially distributed failure rate. | ✓ | ✓ | ||||||
[131] | Proposed a fault-tolerant barrier scheduling scheme that satisfies the Quality-of-Service (QoS) requirements of surveillance applications in the presence of faults. The proposed method is based on a novel fully weighted dynamic graph model that can detect and recover faults. | ✓ | ✓ | ||||||
[132] | Proposed a fault-tolerance approach that combines Static Backup and Dynamic Timing Monitoring (SBDTM) for cluster heads to achieve reliable data acquisition and ensure the reliability of an IoT monitoring system. The proposed method used the Markov model-based cluster head to achieve the reliability of the model. | ✓ | ✓ | ✓ | ✓ | ||||
[133] | Proposed a practical Edge-Intelligent Service Placement Algorithm (EISPA) with the use of Particle Swarm Optimization (PSO).to solve a service continuity problem. The work dealt efficiently with the basic fact that some 5G-and-beyond IIoT applications roam around different regions of the MEC servers. | ✓ | ✓ | ✓ | ✓ | ✓ | |||
[134] | Proposed a solution for the connectivity and robustness in IoT networks during disaster recovery actions using a mobile robot. The proposed method is based on the use of the Optimal Localizable K-Coverage (OLKC) strategies to help in hole recovery. Moreover, the developed work presented two optimality requirements to achieve maximum coverage by the proposed OLKC in an unfamiliar, hostile, or harsh environment using the lowest number of nodes. | ✓ | ✓ | ✓ | ✓ |
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Survey Article | Fault Tolerance Framework Classification | Error Classification | Comparative Study | Open Issues | Specific to a Particular Branch of the WSN | Frameworks | Related Works in Term of Time | ||
---|---|---|---|---|---|---|---|---|---|
1–20 | 20–40 | More than 40 | |||||||
[17] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 1992–2020 | ||
[18] | × | × | ✓ | ✓ | ✓ | ✓ | 2014–2019 | ||
[19] | ✓ | ✓ | ✓ | ✓ | × | ✓ | 2003–2018 | ||
[20] | × | × | × | ✓ | × | ✓ | 2013–2015 | ||
[21] | ✓ | ✓ | ✓ | × | × | ✓ | 2009–2018 | ||
[22] | ✓ | × | × | × | × | ✓ | 2000–2014 | ||
[23] | × | × | ✓ | ✓ | × | ✓ | 2006–2014 | ||
[24] | ✓ | ✓ | ✓ | ✓ | × | ✓ | 2000–2014 | ||
[25] | ✓ | ✓ | ✓ | × | × | ✓ | 2013–2017 | ||
[26] | × | × | ✓ | × | × | ✓ | 2000–2015 | ||
[27] | ✓ | ✓ | ✓ | × | × | ✓ | 2005–2017 | ||
[28] | ✓ | × | ✓ | × | ✓ | ✓ | 2008–2017 | ||
[29] | ✓ | × | × | ✓ | × | ✓ | 2002–2005 | ||
[30] | × | × | ✓ | ✓ | × | ✓ | 2004–2009 | ||
[31] | ✓ | × | ✓ | × | × | ✓ | 2002–2009 | ||
[32] | × | × | ✓ | × | × | ✓ | 2002–2007 | ||
[33] | ✓ | ✓ | ✓ | × | × | ✓ | 2002–2007 | ||
[34] | ✓ | ✓ | ✓ | ✓ | × | ✓ | 2002–2006 |
Survey Article | Main Contribution |
---|---|
[17] | Presented a comprehensive review of fault-tolerant approaches developed for Underwater Sensor Networks (USNs). |
[18] | Presented new future directions and unsolved issues in routing protocols for Flying Ad Hoc Network (VANET). One issue is related to the critical need for having a high fault tolerance ability embedded with routing protocols. |
[19] | Presented a summarization and analysis of many previous fault management frameworks developed and designed for WSN. |
[20] | Presented a review of the fault-tolerant strategies used to create trustworthy WSNs. |
[21] | Presented and analyzed a group of methods for fault detection in WSNs. The study showed a need for a clearer, more accurate, and more comprehensive fault detection and fault tolerance strategy that would maximize the energy savings of the sensor nodes. |
[22] | Presented a discussion on previous and fundamental in the context of time of fault tolerance algorithms that deals with errors and radiation effects on sensor behavior. |
[23] | Presented a study on different fault recovery techniques and analyzed their methodology in terms of energy use. |
[24] | Presented a discussion of some approaches used not just for fault detection but also to prevent faults from occurring, such as data aggregation. The authors classified the fault tolerance approaches according to only two factors: the number of nodes and the region size. |
[25] | Presented a classification of fault diagnosis approaches (From 2013 to 2018) into three categories based on the decision hubs and key characteristics of employed algorithms. |
[26] | Presented an analysis for specific methods in fault tolerance such as deployment, redundancy, and clustering. |
[27] | Presented state of the art for self-healing techniques. The study divided the self-healing mechanisms into four steps: information collection, fault detection, fault classification, and fault recovery. |
[28] | Presented a detail review on the sensor nodes failures detection and fault tolerance in Ambient Assisted Living (AAL) systems based on WSNs. |
[29] | Presented a brief investigation of many problems that a sensor node may encounter with a general classification of fault tolerance structure. |
[30] | Presented a comparative study for several fault management techniques and compared them according to dominant criteria such as overhead, bandwidth, and scalability. |
[31] | Presented a comprehensive review of several approaches to the notion of fault tolerance. The study proposed a categorization for fault frameworks based on the structure of task management. |
[32] | Presented a summarization of the key ideas for existing fault-tolerant techniques in routing protocols in WSNs. |
[33] | Presented a review of frameworks for particular applications and then categorized various fault management according to the types of problems that occur in each implementation. |
[34] | Presented a new approach related to the security risks that must be handled throughout all operating stages of a fault-tolerant system in WSN. |
References | Area of Study | Methodology | Main Performance Metrics |
---|---|---|---|
[2] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | MATLAB |
|
[7] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[13] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[14] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[37] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | NS3 |
|
[45] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[46] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[57] | Industry Revolution (IR 4.0) and Internet of Things (IoT) | Statistical Model |
|
[59] | Wireless Sensor Networks (WSNs) | NS2 |
|
[82] | Wireless Sensor Actor Networks (WSANs) | Castalia |
|
[83] | Wireless Sensor Networks (WSNs) | Python |
|
[84] | Wireless Sensor Networks (WSNs) | OMNET++ |
|
[85] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[86] | Wireless Sensor Networks (WSNs) | Testbed |
|
[87] | Wireless Sensor Networks (WSNs) | Testbed |
|
[88] | Wireless Sensor Networks (WSNs) | Vienna Scientific Cluster VSC |
|
[89] | Wireless Sensor Networks (WSNs) | Castalia |
|
[90] | Underwater Wireless Sensor Networks (UW_WSNs) | NS2 |
|
[91] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[92] | Wireless Sensor Networks (WSNs) | Testbed and TOSSIM |
|
[93] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | NS2 |
|
[94] | Wireless Sensor Networks (WSNs) | Testbed |
|
[95] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | NS2 |
|
[96] | Wireless Sensor Networks (WSNs) | NS2 |
|
[97] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[98] | Wireless Sensor Networks (WSNs) | Testbed |
|
[99] | Wireless Sensor Networks (WSNs) | Testbed and MATLAB |
|
[100] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[101] | Wireless Body Area Network (WBAN) | MATLAB |
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[102] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[103] | Wireless Sensor Networks (WSNs) | NS2 |
|
[104] | Wireless Sensor Networks (WSNs) | Testbed and NS2 |
|
[105] | Wireless Sensor Networks (WSNs) | Testbed |
|
[106] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[107] | Wireless Sensor Networks (WSNs) | OMNET++ |
|
[108] | Wireless Sensor Networks (WSNs) | Testbed |
|
[109] | Wireless Sensor Networks | MATLAB |
|
[110] | Industrial Wireless Sensor Networks (IWSNs) | MATLAB |
|
[111] | Wireless Sensor Networks (WSNs) | NS2 |
|
[112] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[113] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[114] | Wireless Sensor Networks (WSNs) | Testbed |
|
[115] | Wireless Sensor Networks (WSNs) | Testbed and MATLAB |
|
[116] | Wireless Sensor Networks (WSNs) | NS2 |
|
[117] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[118] | Wireless Sensor Networks (WSNs) | NS2 |
|
[119] | Mobile Wireless Sensor Networks (WSNs) | OMNET++ |
|
[120] | Wireless Sensor Networks (WSNs) | NS2 |
|
[121] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[122] | Wireless Sensor Networks (WSNs) | Python |
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[123] | Wireless Sensor Networks (WSNs) | MATLAB |
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[124] | Wireless Sensor Networks (WSNs) | Simulation |
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[125] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | C++ |
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[126] | Wireless Sensor Networks (WSNs) | Monte Carlo and MATLAB |
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[127] | Internet of Things (IoT) | Castalia |
|
[128] | Wireless Sensor Networks (WSNs) | MATLAB |
|
[129] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | NS2 |
|
[130] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | MATLAB |
|
[131] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | NS2 |
|
[132] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | NS3 |
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[133] | 5G, Industrial Internet of Things (IIoT) and Wireless Sensor Networks (WSNs) | Python |
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[134] | Internet of Things (IoT) and Wireless Sensor Networks (WSNs) | MATLAB |
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References | Network Type | Fault Type | Fault Tolerance Approach | Fault Tolerance Procedures | ||
---|---|---|---|---|---|---|
Detection | Diagnosis | Recovery | ||||
[2] | Heterogeneous | Node Faults (CH Failure) | Hybrid Based | Decentralized | Reactive | - |
[7] | Homogeneous | Node Faults | Centralized Based | Self-Supervision | Active | - |
[13] | Homogeneous | Node Faults and Network Faults | Decentralized Based | Self-Supervision and Decentralized | Proactive | Forward |
[14] | Homogeneous | Node Faults | Decentralized Based | Self-Supervision | Reactive | - |
[37] | Heterogeneous | Node Faults (CH Faults) | Decentralized Based | Decentralized | Active-Proactive | - |
[45] | Heterogeneous | Node Faults (CH Failure) | Decentralized Based | Decentralized | Active | - |
[46] | Heterogeneous | Node Faults (CH Faults) and Network Faults (Links) | Decentralized Based | Decentralized | Active | Backward |
[57] | Homogeneous | Node Faults | Centralized Based | Centralized | Passive | - |
[59] | Homogeneous | Node Faults and Network Faults | Decentralized Based | Decentralized | Reactive | - |
[82] | Heterogeneous | Node Faults | Hybrid Based | Decentralized | Active | Backward |
[83] | Homogeneous | Node Faults | Centralized Based | Decentralized | Proactive | - |
[84] | Heterogeneous | Node Faults | Decentralized Based | Self-Supervision | Active | - |
[85] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Reactive | Forward |
[86] | Homogeneous | Node Faults and Network Faults | Centralized Based | Decentralized | Active- Proactive | - |
[87] | Homogeneous | Node Faults | Decentralized Based | Decentralized | Passive | - |
[88] | Homogeneous | Node Faults | Decentralized Based | Decentralized | Reactive | - |
[89] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Reactive | - |
[90] | Heterogeneous | Node Faults | Centralized Based | Self-Supervision | Active | Backward |
[91] | Heterogeneous | Node Faults | Centralized Based | Decentralized | Active | Backward |
[92] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Proactive | |
[93] | Heterogeneous | Network Faults (Link Failure) | Decentralized Based | Decentralized | Reactive | - |
[94] | Homogeneous | Network Faults (Link Failure) | Centralized Based | Centralized | Passive | - |
[95] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Active | Forward |
[96] | Homogeneous | Node Faults and Network Faults | Decentralized Based | Decentralized | Active | - |
[97] | Heterogeneous | Node Faults and Network Faults | Decentralized Based | Decentralized | Active | Backward |
[98] | Heterogeneous | Network Faults (Link Failure) | Decentralized Based | Decentralized | Active | - |
[99] | Heterogeneous | Network Faults (Link Failure) | Decentralized Based | Decentralized | Active | - |
[100] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Proactive | - |
[101] | Homogeneous | Network Faults (Link Failure) | Centralized Based | Centralized | Passive | - |
[102] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Active | - |
[103] | Heterogeneous | Node Faults | Centralized Based | Decentralized | Reactive | - |
[104] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Active | Forward |
[105] | Homogeneous | Node Faults | Centralized Based | Centralized | Active and Proactive | - |
[106] | Homogeneous | Node Faults | Centralized Based | Self-Supervision and Centralized | Passive | Backward |
[107] | Heterogeneous | Node Faults (CH Failure) | Decentralized Based | Self-Supervision and Decentralized | Active | Forward |
[108] | Homogeneous | Node Faults | Centralized Based | Centralized | Active | - |
[109] | Homogeneous | Node Faults and Network Faults | Centralized Based | Self-Supervision | Active | - |
[110] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Active | Backward |
[111] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Active | - |
[112] | Heterogeneous | Node Faults | Hybrid Based | Decentralized | Proactive | - |
[113] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Passive and Active | - |
[114] | Homogeneous | Node Faults | Centralized Based | Centralized | Active | - |
[115] | Homogeneous | Node Faults | Decentralized Based | Centralized | Active | - |
[116] | Heterogeneous | Node Faults | Centralized Based | Decentralized | Passive | Forward |
[117] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Active | Backward |
[118] | Heterogeneous | Node Faults (CH Failure) | Centralized Based | Decentralized | Passive and Active | - |
[119] | Heterogeneous | Node Faults (CH Failure) | Centralized Based | Centralized | Active | Forward |
[120] | Heterogeneous | Node Faults | Decentralized Based | Decentralized | Proactive | Backward |
[121] | Heterogeneous | Node Faults | Hybrid Based | Decentralized | Proactive | Backward |
[122] | Homogeneous | Node Faults | Decentralized Based | Self-Supervision and Decentralized | Proactive | - |
[123] | Heterogeneous | Node Faults and Network Faults | Decentralized Based | Decentralized | Active | - |
[124] | Homogeneous | Node Faults | Decentralized Based | Decentralized | Active | - |
[125] | Heterogeneous | Node Faults | Decentralized Based | Self-Supervision and Decentralized | Passive | - |
[126] | Heterogeneous | Node Faults and Network Faults | Decentralized Based | Self-Supervision | Proactive | - |
[127] | Homogeneous | Node Faults | Centralized Based | Centralized | Active | - |
[128] | Homogeneous | Node Faults | Centralized Based | Self-Supervision and Centralized | Active | - |
[129] | Heterogeneous | Network Faults | Centralized Based | Decentralized | Reactive | - |
[130] | Heterogeneous | Node Faults and Network Faults | Decentralized Based | Self-Supervision and Decentralized | Active | - |
[131] | Homogeneous | Node Faults and Network Faults | Centralized Based | Self-Supervision | Active | Forward |
[132] | Heterogeneous | Node Faults (CH Failure) | Decentralized Based | Decentralized | Active | Backward |
[133] | Heterogeneous | Network Faults | Decentralized Based | Decentralized | Active | - |
[134] | Heterogeneous | Network Faults | Decentralized Based | Self-Supervision and Decentralized | Active | Forward |
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Adday, G.H.; Subramaniam, S.K.; Zukarnain, Z.A.; Samian, N. Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions. Sensors 2022, 22, 6041. https://doi.org/10.3390/s22166041
Adday GH, Subramaniam SK, Zukarnain ZA, Samian N. Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions. Sensors. 2022; 22(16):6041. https://doi.org/10.3390/s22166041
Chicago/Turabian StyleAdday, Ghaihab Hassan, Shamala K. Subramaniam, Zuriati Ahmad Zukarnain, and Normalia Samian. 2022. "Fault Tolerance Structures in Wireless Sensor Networks (WSNs): Survey, Classification, and Future Directions" Sensors 22, no. 16: 6041. https://doi.org/10.3390/s22166041